Using Large Language Models for OntoClean-based Ontology Refinement

TL;DR

This paper explores the integration of Large Language Models such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodology, and suggests the potential for LLMs to enhance ontology refinement.

Abstract

This paper explores the integration of Large Language Models (LLMs) such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodology. OntoClean, critical for assessing the metaphysical quality of ontologies, involves a two-step process of assigning meta-properties to classes and verifying a set of constraints. Manually conducting the first step proves difficult in practice, due to the need for philosophical expertise and lack of consensus among ontologists. By employing LLMs with two prompting strategies, the study demonstrates that high accuracy in the labelling process can be achieved. The findings suggest the potential for LLMs to enhance ontology refinement, proposing the development of plugin software for ontology tools to facilitate this integration.

Figures (3)

• Figure 1: Pipeline for the Human/AI OntoClean refinement process.
• Figure 2: Flat and hierarchical representations of an example ontology.
• Figure 3: Experimental results for different prompts and LLMs. Blue indicates correct and orange incorrect labels.